College of Business Administration, Liaoning Technical University, Huludao, Liaoning 125105, China.
Comput Intell Neurosci. 2022 Apr 10;2022:9423718. doi: 10.1155/2022/9423718. eCollection 2022.
This study aims to speed up the progress of scientific research projects in colleges and universities, continuously improve the innovation ability of scientific research teams in colleges and universities, and optimize the current management methods of performance appraisal of college innovation ability. Firstly, the needs of the innovation performance evaluation system are analyzed, and the corresponding innovation performance evaluation index system of scientific research team is constructed. Secondly, the Internet of Things (IoT) combines the Field Programmable Gate Array (FPGA) to build an innovation capability performance appraisal management terminal. Thirdly, the lightweight deep network has been built into the innovation ability performance assessment management network of university scientific research teams, which relates to the innovation performance assessment index system of scientific research teams. Finally, the system performance is tested. The results show that the proposed method has different degrees of compression for MobileNet, which can significantly reduce the network computation and retain the original recognition ability. Models whose Floating-Point Operations (FLOPs) are reduced by 70% to 90% have 3.6 to 14.3 times fewer parameters. Under different pruning rates, the proposed model has higher model compression rate and recognition accuracy than other models. The results also show that the output of the results is closely related to the interests of the research team. The academic influence score of Team 1 is 0.17, which is the highest among the six groups in this experimental study, indicating that Team 1 has the most significant academic influence. These results provide certain data support and method reference for evaluating the innovation ability of scientific research teams in colleges and universities and contribute to the comprehensive development of efficient scientific research teams.
本研究旨在加快高校科研项目的进展,不断提高高校科研团队的创新能力,优化高校创新能力绩效考核的现行管理方法。首先,分析创新绩效评价体系的需求,构建相应的科研团队创新绩效评价指标体系。其次,将物联网(IoT)与现场可编程门阵列(FPGA)相结合,构建创新能力绩效评估管理终端。再次,将轻量化深度网络构建到高校科研团队创新能力绩效评估管理网络中,涉及科研团队创新绩效评估指标体系。最后,测试系统性能。结果表明,所提出的方法对 MobileNet 有不同程度的压缩,可以显著减少网络计算并保留原始识别能力。FLOPs 减少 70%至 90%的模型参数减少 3.6 至 14.3 倍。在不同的修剪率下,所提出的模型比其他模型具有更高的模型压缩率和识别精度。结果还表明,结果的输出与研究团队的利益密切相关。第 1 组的学术影响力得分为 0.17,在本次实验研究的六组中最高,表明第 1 组的学术影响力最大。这些结果为高校科研团队创新能力评估提供了一定的数据支持和方法参考,有助于高效科研团队的全面发展。